Lars
052ba195cc
- Adjusted the total number of placeholders from 116 to 114 across various documentation and code files to reflect the current state of the system. - Enhanced TDEE calculation logic in `nutrition_metrics.py` to prioritize Mifflin–St Jeor BMR with PAL when demographic data is available, with a fallback to a weight-based estimate. - Updated placeholder registrations to ensure consistency with the new metadata structure and improved data handling. - Revised documentation to clarify the authoritative source of placeholder metadata and the implications of the changes on existing functionalities. These updates improve the accuracy and consistency of the placeholder system and enhance the nutritional assessment capabilities within the application.
103 lines
5.0 KiB
Python
103 lines
5.0 KiB
Python
"""
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Placeholder registration: nutrition_score
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Focus-gewichteter Ernährungs-Meta-Score (separates Modul, um nutrition_part_c schlank zu halten).
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"""
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from placeholder_registry import (
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PlaceholderMetadata,
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MissingValuePolicy,
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EvidenceType,
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OutputType,
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PlaceholderType,
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register_placeholder,
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)
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nutrition_score_metadata = PlaceholderMetadata(
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key="nutrition_score",
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category="Ernährung",
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description="Ernährungs-Score (0–100), gewichtet nach Focus Areas",
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resolver_module="backend/placeholder_resolver.py",
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resolver_function="_safe_int",
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data_layer_module="backend/data_layer/nutrition_metrics.py",
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data_layer_function="calculate_nutrition_score",
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source_tables=[
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"nutrition_log",
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"weight_log",
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"user_focus_area_weights",
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"focus_area_definitions",
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],
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semantic_contract=(
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"Gewichteter Score 0–100 aus Komponenten, die nur einfließen, wenn der Nutzer "
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"passende Ernährungs-Focus-Gewichte gesetzt hat (z. B. protein_intake, "
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"calorie_balance, macro_consistency). Nutzt u. a. Protein-Adequacy, "
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"Makro-Konsistenz, Kalorien-Adhärenz (über Energiebilanz) und Makro-Balance."
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),
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business_meaning=(
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"Verdichteter KPI für Prompts: passt die dokumentierte Ernährung zur "
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"gewichteten strategischen Priorität des Nutzers?"
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),
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unit="score (0-100)",
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time_window="composite (7d / 28d je Komponente)",
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output_type=OutputType.NUMERIC,
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placeholder_type=PlaceholderType.SCORE,
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format_hint="Ganzzahl; bei fehlender Ernährungs-Gewichtung oft nicht verfügbar",
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example_output="72",
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minimum_data_requirements=(
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"Mindestens eine Ernährungs-Focus-Komponente mit Gewicht > 0; "
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"sowie je nach Komponente ausreichende nutrition_log-/weight_log-Abdeckung."
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),
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quality_filter_policy=None,
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confidence_logic=(
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"Kein separates Confidence-Feld im Resolver; fehlende Komponenten werden "
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"aus der Gewichtung ausgeschlossen. total_nutrition_weight == 0 ergibt keinen Score."
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),
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missing_value_policy=MissingValuePolicy(
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available=False,
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value_raw=None,
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missing_reason="not_applicable",
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legacy_display="nicht verfügbar",
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),
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known_limitations=(
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"Abhängig von user_focus_area_weights; ohne Ernährungs-Fokus liefert die "
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"Funktion None. Kalorien-Adhärenz nutzt 7d-Energiebilanz vs. profiles.goal_mode "
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"(weight_loss / strength+recomposition / sonst maintenance). "
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"_score_macro_balance nutzt zeilenbasierte 28d-Abfrage (langfristig an "
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"Tagesaggregation angleichen)."
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),
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layer_1_decision="Data Layer (nutrition_metrics.calculate_nutrition_score)",
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layer_2a_decision="Placeholder Resolver (_safe_int)",
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layer_2b_reuse_possible=True,
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architecture_alignment="Phase 0c: Berechnung in nutrition_metrics",
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issue_53_alignment="Layer 1 als Quelle; Komponenten nutzen weitere Layer-1-Funktionen",
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evidence={},
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)
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nutrition_score_metadata.set_evidence("key", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("category", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("description", EvidenceType.MIXED)
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nutrition_score_metadata.set_evidence("resolver_module", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("resolver_function", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("data_layer_module", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("data_layer_function", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("source_tables", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("semantic_contract", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("business_meaning", EvidenceType.DRAFT_DERIVED)
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nutrition_score_metadata.set_evidence("unit", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("time_window", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("output_type", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("placeholder_type", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("format_hint", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("example_output", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("minimum_data_requirements", EvidenceType.MIXED)
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nutrition_score_metadata.set_evidence("confidence_logic", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("missing_value_policy", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("known_limitations", EvidenceType.MIXED)
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nutrition_score_metadata.set_evidence("layer_1_decision", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("layer_2a_decision", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("layer_2b_reuse_possible", EvidenceType.TO_VERIFY)
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nutrition_score_metadata.set_evidence("architecture_alignment", EvidenceType.CODE_DERIVED)
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nutrition_score_metadata.set_evidence("issue_53_alignment", EvidenceType.MIXED)
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register_placeholder(nutrition_score_metadata)
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